APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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In linear regression. Suppose we set 0=-2, 1=0.5. What is h(4)?
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0
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-1.5
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1.5
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1
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Explanation:
Detailed explanation-1: -What will happen when you fit a degree 4 polynomial in linear regression? Since degree 4 will be more complex(overfitting the data) than the degree 3 model, it will again perfectly fit the data. In such a case, the training error will be zero, but the test error may not be zero.
Detailed explanation-2: -This is called mean normalization. For which case, A or B, was the learning rate likely too large? The cost is increasing as training continues, which likely indicates that the learning rate alpha is too large.
Detailed explanation-3: -Linear Regression Algorithm is a machine learning algorithm based on supervised learning.
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